import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY





@is_pipeline_test
@require_torch_or_tf
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   MODEL_FOR_CAUSAL_LM_MAPPING
		lowerCAmelCase_     =   TF_MODEL_FOR_CAUSAL_LM_MAPPING
		@require_torch
		def   _snake_case       (       self     :     Optional[int]		)      ->	List[Any]:
				_lowerCamelCase				   =  pipeline(task='text-generation'     ,  model='sshleifer/tiny-ctrl'     ,  framework='pt'		)
				# Using `do_sample=False` to force deterministic output
				_lowerCamelCase				   =  text_generator('This is a test'     ,  do_sample=snake_case__		)
				self.assertEqual(
				    snake_case__     ,  [
				        {
				            'generated_text': (
				                'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
				                ' oscope. FiliFili@@'
				            )
				        }
				    ]     ,  )

				_lowerCamelCase				   =  text_generator(['This is a test', 'This is a second test']		)
				self.assertEqual(
				    snake_case__     ,  [
				        [
				            {
				                'generated_text': (
				                    'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
				                    ' oscope. FiliFili@@'
				                )
				            }
				        ],
				        [
				            {
				                'generated_text': (
				                    'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
				                    ' oscope. oscope. FiliFili@@'
				                )
				            }
				        ],
				    ]     ,  )

				_lowerCamelCase				   =  text_generator('This is a test'     ,  do_sample=snake_case__     ,  num_return_sequences=2     ,  return_tensors=snake_case__		)
				self.assertEqual(
				    snake_case__     ,  [
				        {'generated_token_ids': ANY(snake_case__		)},
				        {'generated_token_ids': ANY(snake_case__		)},
				    ]     ,  )
				_lowerCamelCase				   =  text_generator.model.config.eos_token_id
				_lowerCamelCase				   =  '<pad>'
				_lowerCamelCase				   =  text_generator(
				    ['This is a test', 'This is a second test']     ,  do_sample=snake_case__     ,  num_return_sequences=2     ,  batch_size=2     ,  return_tensors=snake_case__     ,  )
				self.assertEqual(
				    snake_case__     ,  [
				        [
				            {'generated_token_ids': ANY(snake_case__		)},
				            {'generated_token_ids': ANY(snake_case__		)},
				        ],
				        [
				            {'generated_token_ids': ANY(snake_case__		)},
				            {'generated_token_ids': ANY(snake_case__		)},
				        ],
				    ]     ,  )
		@require_tf
		def   _snake_case       (       self     :     str		)      ->	Any:
				_lowerCamelCase				   =  pipeline(task='text-generation'     ,  model='sshleifer/tiny-ctrl'     ,  framework='tf'		)

				# Using `do_sample=False` to force deterministic output
				_lowerCamelCase				   =  text_generator('This is a test'     ,  do_sample=snake_case__		)
				self.assertEqual(
				    snake_case__     ,  [
				        {
				            'generated_text': (
				                'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
				                ' please,'
				            )
				        }
				    ]     ,  )

				_lowerCamelCase				   =  text_generator(['This is a test', 'This is a second test']     ,  do_sample=snake_case__		)
				self.assertEqual(
				    snake_case__     ,  [
				        [
				            {
				                'generated_text': (
				                    'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
				                    ' please,'
				                )
				            }
				        ],
				        [
				            {
				                'generated_text': (
				                    'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
				                    ' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
				                )
				            }
				        ],
				    ]     ,  )
		def   _snake_case       (       self     :     Any     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     List[str]		)      ->	List[str]:
				_lowerCamelCase				   =  TextGenerationPipeline(model=snake_case__     ,  tokenizer=snake_case__		)
				return text_generator, ["This is a test", "Another test"]
		def   _snake_case       (       self     :     Any		)      ->	List[Any]:
				_lowerCamelCase				   =  'Hello I believe in'
				_lowerCamelCase				   =  pipeline('text-generation'     ,  model='hf-internal-testing/tiny-random-gpt2'		)
				_lowerCamelCase				   =  text_generator(snake_case__		)
				self.assertEqual(
				    snake_case__     ,  [{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}]     ,  )

				_lowerCamelCase				   =  text_generator(snake_case__     ,  stop_sequence=' fe'		)
				self.assertEqual(snake_case__     ,  [{'generated_text': 'Hello I believe in fe'}]		)
		def   _snake_case       (       self     :     str     ,  snake_case__     :     str     ,  snake_case__     :     Tuple		)      ->	List[str]:
				_lowerCamelCase				   =  text_generator.model
				_lowerCamelCase				   =  text_generator.tokenizer

				_lowerCamelCase				   =  text_generator('This is a test'		)
				self.assertEqual(snake_case__     ,  [{'generated_text': ANY(snake_case__		)}]		)
				self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'		)		)

				_lowerCamelCase				   =  text_generator('This is a test'     ,  return_full_text=snake_case__		)
				self.assertEqual(snake_case__     ,  [{'generated_text': ANY(snake_case__		)}]		)
				self.assertNotIn('This is a test'     ,  outputs[0]['generated_text']		)

				_lowerCamelCase				   =  pipeline(task='text-generation'     ,  model=snake_case__     ,  tokenizer=snake_case__     ,  return_full_text=snake_case__		)
				_lowerCamelCase				   =  text_generator('This is a test'		)
				self.assertEqual(snake_case__     ,  [{'generated_text': ANY(snake_case__		)}]		)
				self.assertNotIn('This is a test'     ,  outputs[0]['generated_text']		)

				_lowerCamelCase				   =  text_generator('This is a test'     ,  return_full_text=snake_case__		)
				self.assertEqual(snake_case__     ,  [{'generated_text': ANY(snake_case__		)}]		)
				self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'		)		)

				_lowerCamelCase				   =  text_generator(['This is great !', 'Something else']     ,  num_return_sequences=2     ,  do_sample=snake_case__		)
				self.assertEqual(
				    snake_case__     ,  [
				        [{'generated_text': ANY(snake_case__		)}, {'generated_text': ANY(snake_case__		)}],
				        [{'generated_text': ANY(snake_case__		)}, {'generated_text': ANY(snake_case__		)}],
				    ]     ,  )

				if text_generator.tokenizer.pad_token is not None:
						_lowerCamelCase				   =  text_generator(
						    ['This is great !', 'Something else']     ,  num_return_sequences=2     ,  batch_size=2     ,  do_sample=snake_case__		)
						self.assertEqual(
						    snake_case__     ,  [
						        [{'generated_text': ANY(snake_case__		)}, {'generated_text': ANY(snake_case__		)}],
						        [{'generated_text': ANY(snake_case__		)}, {'generated_text': ANY(snake_case__		)}],
						    ]     ,  )

				with self.assertRaises(snake_case__		):
						_lowerCamelCase				   =  text_generator('test'     ,  return_full_text=snake_case__     ,  return_text=snake_case__		)
				with self.assertRaises(snake_case__		):
						_lowerCamelCase				   =  text_generator('test'     ,  return_full_text=snake_case__     ,  return_tensors=snake_case__		)
				with self.assertRaises(snake_case__		):
						_lowerCamelCase				   =  text_generator('test'     ,  return_text=snake_case__     ,  return_tensors=snake_case__		)

				# Empty prompt is slighly special
				# it requires BOS token to exist.
				# Special case for Pegasus which will always append EOS so will
				# work even without BOS.
				if (
				    text_generator.tokenizer.bos_token_id is not None
				    or "Pegasus" in tokenizer.__class__.__name__
				    or "Git" in model.__class__.__name__
				):
						_lowerCamelCase				   =  text_generator(''		)
						self.assertEqual(snake_case__     ,  [{'generated_text': ANY(snake_case__		)}]		)
				else:
						with self.assertRaises((ValueError, AssertionError)		):
								_lowerCamelCase				   =  text_generator(''		)

				if text_generator.framework == "tf":
						# TF generation does not support max_new_tokens, and it's impossible
						# to control long generation with only max_length without
						# fancy calculation, dismissing tests for now.
						return
				# We don't care about infinite range models.
				# They already work.
				# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
				_lowerCamelCase				   =  ['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
				if (
				    tokenizer.model_max_length < 1_0_0_0_0
				    and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
				):
						# Handling of large generations
						with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)		):
								text_generator('This is a test' * 5_0_0     ,  max_new_tokens=2_0		)

						_lowerCamelCase				   =  text_generator('This is a test' * 5_0_0     ,  handle_long_generation='hole'     ,  max_new_tokens=2_0		)
						# Hole strategy cannot work
						with self.assertRaises(snake_case__		):
								text_generator(
								    'This is a test' * 5_0_0     ,  handle_long_generation='hole'     ,  max_new_tokens=tokenizer.model_max_length + 1_0     ,  )
		@require_torch
		@require_accelerate
		@require_torch_gpu
		def   _snake_case       (       self     :     List[str]		)      ->	Dict:
				import torch

				# Classic `model_kwargs`
				_lowerCamelCase				   =  pipeline(
				    model='hf-internal-testing/tiny-random-bloom'     ,  model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa}     ,  )
				self.assertEqual(pipe.model.device     ,  torch.device(0		)		)
				self.assertEqual(pipe.model.lm_head.weight.dtype     ,  torch.bfloataa		)
				_lowerCamelCase				   =  pipe('This is a test'		)
				self.assertEqual(
				    snake_case__     ,  [
				        {
				            'generated_text': (
				                'This is a test test test test test test test test test test test test test test test test'
				                ' test'
				            )
				        }
				    ]     ,  )

				# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
				_lowerCamelCase				   =  pipeline(model='hf-internal-testing/tiny-random-bloom'     ,  device_map='auto'     ,  torch_dtype=torch.bfloataa		)
				self.assertEqual(pipe.model.device     ,  torch.device(0		)		)
				self.assertEqual(pipe.model.lm_head.weight.dtype     ,  torch.bfloataa		)
				_lowerCamelCase				   =  pipe('This is a test'		)
				self.assertEqual(
				    snake_case__     ,  [
				        {
				            'generated_text': (
				                'This is a test test test test test test test test test test test test test test test test'
				                ' test'
				            )
				        }
				    ]     ,  )

				# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
				_lowerCamelCase				   =  pipeline(model='hf-internal-testing/tiny-random-bloom'     ,  device_map='auto'		)
				self.assertEqual(pipe.model.device     ,  torch.device(0		)		)
				self.assertEqual(pipe.model.lm_head.weight.dtype     ,  torch.floataa		)
				_lowerCamelCase				   =  pipe('This is a test'		)
				self.assertEqual(
				    snake_case__     ,  [
				        {
				            'generated_text': (
				                'This is a test test test test test test test test test test test test test test test test'
				                ' test'
				            )
				        }
				    ]     ,  )
		@require_torch
		@require_torch_gpu
		def   _snake_case       (       self     :     Optional[Any]		)      ->	int:
				import torch

				_lowerCamelCase				   =  pipeline(model='hf-internal-testing/tiny-random-bloom'     ,  device=0     ,  torch_dtype=torch.floataa		)
				pipe('This is a test'		)
		@require_torch
		@require_accelerate
		@require_torch_gpu
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				import torch

				_lowerCamelCase				   =  pipeline(model='hf-internal-testing/tiny-random-bloom'     ,  device_map='auto'     ,  torch_dtype=torch.floataa		)
				pipe('This is a test'     ,  do_sample=snake_case__     ,  top_p=0.5		)



		def   _snake_case       (       self     :     str		)      ->	Optional[int]:
				_lowerCamelCase				   =  'Hello world'
				_lowerCamelCase				   =  pipeline('text-generation'     ,  model='hf-internal-testing/tiny-random-gpt2'		)
				if text_generator.model.framework == "tf":
						_lowerCamelCase				   =  logging.get_logger('transformers.generation.tf_utils'		)
				else:
						_lowerCamelCase				   =  logging.get_logger('transformers.generation.utils'		)
				_lowerCamelCase				   =  'Both `max_new_tokens`'  # The beggining of the message to be checked in this test

				# Both are set by the user -> log warning
				with CaptureLogger(snake_case__		) as cl:
						_lowerCamelCase				   =  text_generator(snake_case__     ,  max_length=1_0     ,  max_new_tokens=1		)
				self.assertIn(snake_case__     ,  cl.out		)

				# The user only sets one -> no warning
				with CaptureLogger(snake_case__		) as cl:
						_lowerCamelCase				   =  text_generator(snake_case__     ,  max_new_tokens=1		)
				self.assertNotIn(snake_case__     ,  cl.out		)

				with CaptureLogger(snake_case__		) as cl:
						_lowerCamelCase				   =  text_generator(snake_case__     ,  max_length=1_0		)
				self.assertNotIn(snake_case__     ,  cl.out		)
import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]							)   ->    Optional[Any]:
		_lowerCamelCase				   =  3_84
		_lowerCamelCase				   =  7
		if "tiny" in model_name:
				_lowerCamelCase				   =  96
				_lowerCamelCase				   =  (2, 2, 6, 2)
				_lowerCamelCase				   =  (3, 6, 12, 24)
		elif "small" in model_name:
				_lowerCamelCase				   =  96
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (3, 6, 12, 24)
		elif "base" in model_name:
				_lowerCamelCase				   =  1_28
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (4, 8, 16, 32)
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  5_12
		elif "large" in model_name:
				_lowerCamelCase				   =  1_92
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (6, 12, 24, 48)
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  7_68

		# set label information
		_lowerCamelCase				   =  1_50
		_lowerCamelCase				   =  'huggingface/label-files'
		_lowerCamelCase				   =  'ade20k-id2label.json'
		_lowerCamelCase				   =  json.load(open(hf_hub_download(UpperCamelCase			,       UpperCamelCase			,       repo_type='dataset'							)			,       'r'							)							)
		_lowerCamelCase				   =  {int(UpperCamelCase							): v for k, v in idalabel.items()}
		_lowerCamelCase				   =  {v: k for k, v in idalabel.items()}

		_lowerCamelCase				   =  SwinConfig(
		    embed_dim=UpperCamelCase			,       depths=UpperCamelCase			,       num_heads=UpperCamelCase			,       window_size=UpperCamelCase			,       out_features=['stage1', 'stage2', 'stage3', 'stage4']			,       )
		_lowerCamelCase				   =  UperNetConfig(
		    backbone_config=UpperCamelCase			,       auxiliary_in_channels=UpperCamelCase			,       num_labels=UpperCamelCase			,       idalabel=UpperCamelCase			,       labelaid=UpperCamelCase			,       )

		return config


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    int:
		_lowerCamelCase				   =  []

		# fmt: off
		# stem
		rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')							)
		rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')							)
		rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')							)
		rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')							)
		# stages
		for i in range(len(config.backbone_config.depths							)							):
				for j in range(config.backbone_config.depths[i]							):
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")							)

				if i < 3:
						rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.bias""")							)
				rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")							)
				rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")							)

		# decode head
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ]							)
		# fmt: on

		return rename_keys


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				Any			,       UpperCamelCase     :				Any							)   ->    str:
		_lowerCamelCase				   =  dct.pop(UpperCamelCase							)
		_lowerCamelCase				   =  val


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Tuple							)   ->    str:
		_lowerCamelCase				   =  [int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
		for i in range(len(backbone_config.depths							)							):
				_lowerCamelCase				   =  num_features[i]
				for j in range(backbone_config.depths[i]							):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						_lowerCamelCase				   =  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""							)
						_lowerCamelCase				   =  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""							)
						# next, add query, keys and values (in that order) to the state dict
						_lowerCamelCase				   =  in_proj_weight[:dim, :]
						_lowerCamelCase				   =  in_proj_bias[: dim]
						_lowerCamelCase				   =  in_proj_weight[
						    dim : dim * 2, :
						]
						_lowerCamelCase				   =  in_proj_bias[
						    dim : dim * 2
						]
						_lowerCamelCase				   =  in_proj_weight[
						    -dim :, :
						]
						_lowerCamelCase				   =  in_proj_bias[-dim :]
						# fmt: on


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Optional[int]:
		_lowerCamelCase   ,							_lowerCamelCase				   =  x.shape
		_lowerCamelCase				   =  x.reshape(UpperCamelCase			,       4			,       in_channel // 4							)
		_lowerCamelCase				   =  x[:, [0, 2, 1, 3], :].transpose(1			,       2							).reshape(UpperCamelCase			,       UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    int:
		_lowerCamelCase   ,							_lowerCamelCase				   =  x.shape
		_lowerCamelCase				   =  x.reshape(UpperCamelCase			,       in_channel // 4			,       4							)
		_lowerCamelCase				   =  x[:, :, [0, 2, 1, 3]].transpose(1			,       2							).reshape(UpperCamelCase			,       UpperCamelCase							)

		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    List[str]:
		_lowerCamelCase				   =  x.shape[0]
		_lowerCamelCase				   =  x.reshape(4			,       in_channel // 4							)
		_lowerCamelCase				   =  x[[0, 2, 1, 3], :].transpose(0			,       1							).reshape(UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  x.shape[0]
		_lowerCamelCase				   =  x.reshape(in_channel // 4			,       4							)
		_lowerCamelCase				   =  x[:, [0, 2, 1, 3]].transpose(0			,       1							).reshape(UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				int			,       UpperCamelCase     :				Dict							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  {
		    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
		    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
		    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
		    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
		}
		_lowerCamelCase				   =  model_name_to_url[model_name]
		_lowerCamelCase				   =  torch.hub.load_state_dict_from_url(UpperCamelCase			,       map_location='cpu'			,       file_name=UpperCamelCase							)[
		    'state_dict'
		]

		for name, param in state_dict.items():
				print(UpperCamelCase			,       param.shape							)

		_lowerCamelCase				   =  get_upernet_config(UpperCamelCase							)
		_lowerCamelCase				   =  UperNetForSemanticSegmentation(UpperCamelCase							)
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
				if "bn" in key:
						_lowerCamelCase				   =  key.replace('bn'			,       'batch_norm'							)
				_lowerCamelCase				   =  val

		# rename keys
		_lowerCamelCase				   =  create_rename_keys(UpperCamelCase							)
		for src, dest in rename_keys:
				rename_key(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
		read_in_q_k_v(UpperCamelCase			,       config.backbone_config							)

		# fix downsample parameters
		for key, value in state_dict.items():
				if "downsample" in key:
						if "reduction" in key:
								_lowerCamelCase				   =  reverse_correct_unfold_reduction_order(UpperCamelCase							)
						if "norm" in key:
								_lowerCamelCase				   =  reverse_correct_unfold_norm_order(UpperCamelCase							)

		model.load_state_dict(UpperCamelCase							)

		# verify on image
		_lowerCamelCase				   =  'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
		_lowerCamelCase				   =  Image.open(requests.get(UpperCamelCase			,       stream=UpperCamelCase							).raw							).convert('RGB'							)

		_lowerCamelCase				   =  SegformerImageProcessor()
		_lowerCamelCase				   =  processor(UpperCamelCase			,       return_tensors='pt'							).pixel_values

		with torch.no_grad():
				_lowerCamelCase				   =  model(UpperCamelCase							)
				_lowerCamelCase				   =  outputs.logits

		print(logits.shape							)
		print('First values of logits:'			,       logits[0, 0, :3, :3]							)
		# assert values
		if model_name == "upernet-swin-tiny":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]							)
		elif model_name == "upernet-swin-small":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]							)
		elif model_name == "upernet-swin-base":
				_lowerCamelCase				   =  torch.tensor(
				    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]							)
		elif model_name == "upernet-swin-large":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]							)
		print('Logits:'			,       outputs.logits[0, 0, :3, :3]							)
		assert torch.allclose(outputs.logits[0, 0, :3, :3]			,       UpperCamelCase			,       atol=1e-4							)
		print('Looks ok!'							)

		if pytorch_dump_folder_path is not None:
				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""							)
				model.save_pretrained(UpperCamelCase							)
				print(F"""Saving processor to {pytorch_dump_folder_path}"""							)
				processor.save_pretrained(UpperCamelCase							)

		if push_to_hub:
				print(F"""Pushing model and processor for {model_name} to hub"""							)
				model.push_to_hub(F"""openmmlab/{model_name}"""							)
				processor.push_to_hub(F"""openmmlab/{model_name}"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='upernet-swin-tiny',
				    type=str,
				    choices=[F'''upernet-swin-{size}''' for size in ['tiny', 'small', 'base', 'large']],
				    help='Name of the Swin + UperNet model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				A						=			parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)